# Cross-Scan Mamba with Masked Training for Robust Spectral Imaging

arXiv: https://arxiv.org/abs/2408.00629 · HTML: https://mlsanity.com/paper/2408.00629/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Hyperspectral image analysis with Mamba, one of 10 directions in State-space sequence models (S4, Mamba).
- Builds on 11 of its 54 references that are in the map.
- First posted on arXiv 1 Aug 2024.

## Placed in

- State-space sequence models (S4, Mamba) / [Hyperspectral image analysis with Mamba](https://mlsanity.com/ssm/hyperspectral-image-analysis-with-mamba/)

## Builds on (most cited first, 11 of 11)

- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model](https://mlsanity.com/paper/2401.09417/) (2024)
- [VMamba: Visual State Space Model](https://mlsanity.com/paper/2401.10166/) (2024)
- [MambaIR: A Simple Baseline for Image Restoration with State-Space Model](https://mlsanity.com/paper/2402.15648/) (2024)
- [LocalMamba: Visual State Space Model with Windowed Selective Scan](https://mlsanity.com/paper/2403.09338/) (2024)
- [VmambaIR: Visual State Space Model for Image Restoration](https://mlsanity.com/paper/2403.11423/) (2024)
- [State Space Model for New-Generation Network Alternative to Transformers: A Survey](https://mlsanity.com/paper/2404.09516/) (2024)
- [LightM-UNet: Mamba Assists in Lightweight UNet for Medical Image Segmentation](https://mlsanity.com/paper/2403.05246/) (2024)
- [CU-Mamba: Selective State Space Models with Channel Learning for Image Restoration](https://mlsanity.com/paper/2404.11778/) (2024)
- [Spectral-Spatial Mamba for Hyperspectral Image Classification](https://mlsanity.com/paper/2404.18401/) (2024)
- [S2Mamba: A Spatial-spectral State Space Model for Hyperspectral Image Classification](https://mlsanity.com/paper/2404.18213/) (2024)

## Abstract (arXiv)

Snapshot Compressive Imaging (SCI) enables fast spectral imaging but requires effective decoding algorithms for hyperspectral image (HSI) reconstruction from compressed measurements. Current CNN-based methods are limited in modeling long-range dependencies, while Transformer-based models face high computational complexity. Although recent Mamba models outperform CNNs and Transformers in RGB tasks concerning computational efficiency or accuracy, they are not specifically optimized to fully leverage the local spatial and spectral correlations inherent in HSIs. To address this, we propose the Cross-Scanning Mamba, named CS-Mamba, that employs a Spatial-Spectral SSM for global-local balanced context encoding and cross-channel interaction promotion. Besides, while current reconstruction algorithms perform increasingly well in simulation scenarios, they exhibit suboptimal performance on real data due to limited generalization capability. During the training process, the model may not capture the inherent features of the images but rather learn the parameters to mitigate specific noise and loss, which may lead to a decline in reconstruction quality when faced with real scenes. To overcome this challenge, we propose a masked training method to enhance the generalization ability of models. Experiment results show that our CS-Mamba achieves state-of-the-art performance and the masked training method can better reconstruct smooth features to improve the visual quality.